@Article{Cramer:2017:ESA,
author = "Sam Cramer and Michael Kampouridis and
Alex A. Freitas and Antonis K. Alexandridis",
title = "An extensive evaluation of seven machine learning
methods for rainfall prediction in weather
derivatives",
journal = "Expert Systems with Applications",
year = "2017",
ISSN = "0957-4174",
DOI = "doi:10.1016/j.eswa.2017.05.029",
URL = "http://www.sciencedirect.com/science/article/pii/S0957417417303457",
abstract = "Regression problems provide some of the most
challenging research opportunities in the area of
machine learning, and more broadly intelligent systems,
where the predictions of some target variables are
critical to a specific application. Rainfall is a prime
example, as it exhibits unique characteristics of high
volatility and chaotic patterns that do not exist in
other time series data. This work's main impact is to
show the benefit machine learning algorithms, and more
broadly intelligent systems have over the current
state-of-the-art techniques for rainfall prediction
within rainfall derivatives. We apply and compare the
predictive performance of the current state-of-the-art
(Markov chain extended with rainfall prediction) and
six other popular machine learning algorithms, namely:
Genetic Programming, Support Vector Regression, Radial
Basis Neural Networks, M5 Rules, M5 Model trees, and
k-Nearest Neighbours. To assist in the extensive
evaluation, we run tests using the rainfall time series
across data sets for 42 cities, with very diverse
climatic features. This thorough examination shows that
the machine learning methods are able to outperform the
current state-of-the-art. Another contribution of this
work is to detect correlations between different
climates and predictive accuracy. Thus, these results
show the positive effect that machine learning-based
intelligent systems have for predicting rainfall based
on predictive accuracy and with minimal correlations
existing across climates.",
keywords = "genetic algorithms, genetic programming, Weather
derivatives, Rainfall, Machine learning",
}